This chapter deals with neural networks (NN), starting from the early days of the perceptron and perceptron rule, then moves on to review multilayer feed-forward neural networks and the backpropagation algorithm. The drawbacks of training NN with many layers, via the backpropagation algorithm, are discussed together with the advantages that one would expect to obtain if such networks could be trained efficiently. Restricted Boltzmann machines (RBM) are then discussed and the contrastive divergence algorithm is presented as the vehicle to pre-train deep/many layer NN architectures. Deep belief networks, conditional RBMs and autoencoders are also discussed. Finally, two case studies are presented, concerning the use of deep NN architectures, one in the context of OCR and another in the context of autoencoding.

Neural Networks and Deep Learning

A new research area, Inductive Logic Programming, is presently emerging. While inheriting various positive characteristics of the parent subjects of Logic Programming and Machine Learning, it is hoped that the new area will overcome many of the limitations of its forebears. The background to present developments within this area is discussed and various goals and aspirations for the increasing body of researchers are identified. Inductive Logic Programming needs to be based on sound principles from both Logic and Statistics. On the side of statistical justification of hypotheses we discuss the possible relationship between Algorithmic Complexity theory and Probably-Approximately-Correct (PAC) Learning. In terms of logic we provide a unifying framework for Muggleton and Buntine’s Inverse Resolution (IR) and Plotkin’s Relative Least General Generalisation (RLGG) by rederiving RLGG in terms of IR. This leads to a discussion of the feasibility of extending the RLGG framework to allow for the invention of new predicates, previously discussed only within the context of IR.

Inductive logic programming

Web stores, where buyers place orders over the Internet, have emerged to become a prevalent sales channel. In this research, we developed neural network models, which are known for their capability of modeling noncompensatory decision processes, to predict and explain consumer choice between web and traditional stores. We conducted an empirical survey for the study. Specifically, in the survey, the purchases of six distinct products from web stores were contrasted with the corresponding purchases from traditional stores. The respondents' perceived attribute performance was then used to predict the customers' channel choice between web and traditional stores. We have provided statistical evidence that neural networks significantly outperform logistic regression models for most of the surveyed products in terms of the predicting power. To gain more insights from the models, we have identified the factors that have significant impact on customers' channel attitude through sensitivity analyses on the neural networks. The results indicate that the influential factors are different across product categories. The findings of the study offer a number of implications for channel management.

/pdf/predicting-and-explaining-patronage-behavior-toward-web-and-3gmukxzq6a.pdf

Predicting and explaining patronage behavior toward web and traditional stores using neural networks: a comparative analysis with logistic regression

Dynamic process fault monitoring based on neural network and PCA

Artificial neural network approach to predict the mechanical properties of Cu–Sn–Pb–Zn–Ni cast alloys

Chapter 17 – introduction to artificial neural networks

Chapter 11 – prolog in artificial intelligence

Chapter 10 – introduction to artificial intelligence

Chapter 16 – overview of knowledge-based applications in chemical engineering

Chapter 12 – introduction to exsep

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